Building management system with dynamic energy prediction model updates

ABSTRACT

A building management system including building equipment operable to affect a variable state or condition of a building. The building management system includes a controller including a processing circuit. The processing circuit is configured to obtain an energy prediction model (EPM) for predicting energy requirements over time. The processing circuit is configured to monitor one or more triggering events to determine if the EPM should be retrained. The processing circuit is configured to, in response to detecting that a triggering event has occurred, identify updated values of one or more hyper-parameters of the EPM. The processing circuit is configured to operate the building equipment based on the EPM.

BACKGROUND

The present disclosure relates generally building equipment of abuilding and more particularly to a building management system foroperating building equipment based on artificial intelligence models.

Operating building equipment can be costly for building owners and canconsume large amount of energy from an electricity grid. If operatedbased on standard control schemes (e.g., a constant setpoint), buildingequipment may consume unnecessary amounts of electricity and/or otherresources. Further, due to changes in energy costs over time, buildingowners may be forced to pay unnecessary amounts to operate buildingequipment.

SUMMARY

One implementation of the present disclosure is a building managementsystem, according to some embodiments. The building management systemincludes building equipment operable to affect a variable state orcondition of a building, according to some embodiments. The buildingmanagement system includes a controller including a processing circuit,according to some embodiments. The processing circuit is configured toobtain an energy prediction model (EPM) for predicting energyrequirements over time, according to some embodiments. The processingcircuit is configured to monitor one or more triggering events todetermine if the EPM should be retrained, according to some embodiments.The processing circuit is configured to, in response to detecting that atriggering event has occurred, identify updated values of one or morehyper-parameters of the EPM, according to some embodiments. Theprocessing circuit is configured to operate the building equipment basedon the EPM, according to some embodiments.

In some embodiments, the processing circuit is configured to calculate aperformance metric of the EPM. The triggering event includes theperformance metric exceeding a threshold value, according to someembodiments.

In some embodiments, the processing circuit is configured to compare theEPM associated with the updated values of the one or morehyper-parameters to an existing EPM to determine what EPM generates moreaccurate predictions. The processing circuit is configured to deploy theEPM that generates more accurate predictions, according to someembodiments.

In some embodiments, the processing circuit is configured to pre-processoperational data of the building equipment. The updated values of theone or more hyper-parameters are determined based on the pre-processedoperational data, according to some embodiments.

In some embodiments, the processing circuit is configured to retrain theEPM based on data describing operation of the building equipment. TheEPM is retrained respective to a proportion of old data and new data,according to some embodiments.

In some embodiments, the one or more hyper-parameters include at leastone of a transfer learning flag or a training window.

In some embodiments, the processing circuit is further configured toswitch to an older EPM in response to determining that the older EPM ismore accurate than the EPM.

Another implementation of the present disclosure is a method foroperating building equipment of a building, according to someembodiments. The method includes obtaining an energy prediction model(EPM) for predicting energy requirements over time, according to someembodiments. The method includes monitoring one or more triggeringevents to determine if the EPM should be retrained, according to someembodiments. The method includes, in response to detecting that atriggering event has occurred, identifying updated values of one or morehyper-parameters of the EPM, according to some embodiments. The methodincludes operating the building equipment based on the EPM to affect avariable state or condition of the building, according to someembodiments.

In some embodiments, the method includes calculating a performancemetric of the EPM. The triggering event includes the performance metricexceeding a threshold value, according to some embodiments.

In some embodiments, the method includes comparing the EPM associatedwith the updated values of the one or more hyper-parameters to anexisting EPM to determine what EPM generates more accurate predictions.The method includes deploying the EPM that generates more accuratepredictions, according to some embodiments.

In some embodiments, the method includes pre-processing operational dataof the building equipment. The updated values of the one or morehyper-parameters are determined based on the pre-processed operationaldata, according to some embodiments.

In some embodiments, the method includes retraining the EPM based ondata describing operation of the building equipment. The EPM isretrained respective to a proportion of old data and new data, accordingto some embodiments.

In some embodiments, the one or more hyper-parameters include at leastone of a transfer learning flag or a training window.

In some embodiments, the method includes switching to an older EPM inresponse to determining that the older EPM is more accurate than theEPM.

Another implementation of the present disclosure is a method fordeploying an energy prediction model (EPM) for equipment, according tosome embodiments. The method includes obtaining the EPM for predictingenergy requirements over time, according to some embodiments. The methodincludes monitoring one or more triggering events to determine if theEPM should be retrained, according to some embodiments. The methodincludes, in response to detecting that a triggering event has occurred,identifying updated values of one or more hyper-parameters of the EPM,according to some embodiments. The method includes deploying the EPM forthe equipment, according to some embodiments.

In some embodiments, the method includes calculating a performancemetric of the EPM. The triggering event includes the performance metricexceeding a threshold value, according to some embodiments.

In some embodiments, the method includes comparing the EPM associatedwith the updated values of the one or more hyper-parameters to anexisting EPM to determine what EPM generates more accurate predictions.The method includes deploying the EPM that generates more accuratepredictions, according to some embodiments.

In some embodiments, the method includes pre-processing operational dataof the equipment. The updated values of the one or more hyper-parametersare determined based on the pre-processed operational data, according tosome embodiments.

In some embodiments, the method includes retraining the EPM based ondata describing operation of the building equipment. The EPM isretrained respective to a proportion of old data and new data, accordingto some embodiments.

In some embodiments, the one or more hyper-parameters include at leastone of a transfer learning flag or a training window.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a building equipped with a HVAC system, accordingto some embodiments.

FIG. 2 is a block diagram of a waterside system which can be used toserve the heating or cooling loads of the building of FIG. 1, accordingto some embodiments.

FIG. 3 is a block diagram of an airside system which can be used toserve the heating or cooling loads of the building of FIG. 1, accordingto some embodiments.

FIG. 4 is a block diagram of a building management system (BMS) whichcan be used to monitor and control the building of FIG. 1, according tosome embodiments.

FIG. 5 is a block diagram of another BMS which can be used to monitorand control the building of FIG. 1, according to some embodiments.

FIG. 6 is a block diagram of an energy prediction controller, accordingto some embodiments.

FIG. 7 is a block diagram of an external factor monitor of the energyprediction controller of FIG. 6, according to some embodiments.

FIG. 8 is a block diagram of a performance metric calculator of theenergy prediction controller of FIG. 6, according to some embodiments.

FIG. 9 is a block diagram of a dynamic hyper-parameter search module ofthe energy prediction controller of FIG. 6, according to someembodiments.

FIG. 10 is a block diagram of a model selector of the energy predictioncontroller of FIG. 6, according to some embodiments.

FIG. 11 is a flow diagram of a process for deploying an energyprediction model, according to some embodiments.

DETAILED DESCRIPTION

Building HVAC Systems and Building Management Systems

Referring now to FIGS. 1-5, several building management systems (BMS)and HVAC systems in which the systems and methods of the presentdisclosure can be implemented are shown, according to some embodiments.In brief overview, FIG. 1 shows a building 10 equipped with a HVACsystem 100. FIG. 2 is a block diagram of a waterside system 200 whichcan be used to serve building 10. FIG. 3 is a block diagram of anairside system 300 which can be used to serve building 10. FIG. 4 is ablock diagram of a BMS which can be used to monitor and control building10. FIG. 5 is a block diagram of another BMS which can be used tomonitor and control building 10.

Building and HVAC System

Referring particularly to FIG. 1, a perspective view of a building 10 isshown. Building 10 is served by a BMS. A BMS is, in general, a system ofdevices configured to control, monitor, and manage equipment in oraround a building or building area. A BMS can include, for example, aHVAC system, a security system, a lighting system, a fire alertingsystem, any other system that is capable of managing building functionsor devices, or any combination thereof.

The BMS that serves building 10 includes a HVAC system 100. HVAC system100 can include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which can be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Waterside System

Referring now to FIG. 2, a block diagram of a waterside system 200 isshown, according to some embodiments. In various embodiments, watersidesystem 200 may supplement or replace waterside system 120 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, waterside system 200 can include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and may operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 can belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2, waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve thermal energy loads (e.g.,hot water, cold water, heating, cooling, etc.) of a building or campus.For example, heater subplant 202 can be configured to heat water in ahot water loop 214 that circulates the hot water between heater subplant202 and building 10. Chiller subplant 206 can be configured to chillwater in a cold water loop 216 that circulates the cold water betweenchiller subplant 206 building 10. Heat recovery chiller subplant 204 canbe configured to transfer heat from cold water loop 216 to hot waterloop 214 to provide additional heating for the hot water and additionalcooling for the cold water. Condenser water loop 218 may absorb heatfrom the cold water in chiller subplant 206 and reject the absorbed heatin cooling tower subplant 208 or transfer the absorbed heat to hot waterloop 214. Hot TES subplant 210 and cold TES subplant 212 may store hotand cold thermal energy, respectively, for subsequent use.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) can be used inplace of or in addition to water to serve thermal energy loads. In otherembodiments, subplants 202-212 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present disclosure.

Each of subplants 202-212 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 can includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Airside System

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to some embodiments. In various embodiments, airsidesystem 300 may supplement or replace airside system 130 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 can include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 300 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3, airside system 300 is shown to include an economizer-type airhandling unit (AHU) 302. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 302 may receive return air 304 from building zone 306via return air duct 308 and may deliver supply air 310 to building zone306 via supply air duct 312. In some embodiments, AHU 302 is a rooftopunit located on the roof of building 10 (e.g., AHU 106 as shown inFIG. 1) or otherwise positioned to receive both return air 304 andoutside air 314. AHU 302 can be configured to operate exhaust air damper316, mixing damper 318, and outside air damper 320 to control an amountof outside air 314 and return air 304 that combine to form supply air310. Any return air 304 that does not pass through mixing damper 318 canbe exhausted from AHU 302 through exhaust damper 316 as exhaust air 322.

Each of dampers 316-320 can be operated by an actuator. For example,exhaust air damper 316 can be operated by actuator 324, mixing damper318 can be operated by actuator 326, and outside air damper 320 can beoperated by actuator 328. Actuators 324-328 may communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 mayreceive control signals from AHU controller 330 and may provide feedbacksignals to AHU controller 330. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 324-328. AHUcontroller 330 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 can be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 may communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 may receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and may return thechilled fluid to waterside system 200 via piping 344. Valve 346 can bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 may receive a heated fluid from waterside system 200(e.g., from hot water loop 214) via piping 348 and may return the heatedfluid to waterside system 200 via piping 350. Valve 352 can bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 can be controlled by an actuator. Forexample, valve 346 can be controlled by actuator 354 and valve 352 canbe controlled by actuator 356. Actuators 354-356 may communicate withAHU controller 330 via communications links 358-360. Actuators 354-356may receive control signals from AHU controller 330 and may providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 may also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU 330 maycontrol the temperature of supply air 310 and/or building zone 306 byactivating or deactivating coils 334-336, adjusting a speed of fan 338,or a combination of both.

Still referring to FIG. 3, airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 can include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, waterside system200, HVAC system 100, and/or other controllable systems that servebuilding 10. BMS controller 366 may communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 100, a securitysystem, a lighting system, waterside system 200, etc.) via acommunications link 370 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMScontroller 366 can be separate (as shown in FIG. 3) or integrated. In anintegrated implementation, AHU controller 330 can be a software moduleconfigured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 may provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 can be a stationary terminal or amobile device. For example, client device 368 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 may communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Building Management Systems

Referring now to FIG. 4, a block diagram of a building management system(BMS) 400 is shown, according to some embodiments. BMS 400 can beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 400 is shown to include BMS controller 366 and aplurality of building subsystems 428. Building subsystems 428 are shownto include a building electrical subsystem 434, an informationcommunication technology (ICT) subsystem 436, a security subsystem 438,a HVAC subsystem 440, a lighting subsystem 442, a lift/escalatorssubsystem 432, and a fire safety subsystem 430. In various embodiments,building subsystems 428 can include fewer, additional, or alternativesubsystems. For example, building subsystems 428 may also oralternatively include a refrigeration subsystem, an advertising orsignage subsystem, a cooking subsystem, a vending subsystem, a printeror copy service subsystem, or any other type of building subsystem thatuses controllable equipment and/or sensors to monitor or controlbuilding 10. In some embodiments, building subsystems 428 includewaterside system 200 and/or airside system 300, as described withreference to FIGS. 2-3.

Each of building subsystems 428 can include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 440 can include many of the samecomponents as HVAC system 100, as described with reference to FIGS. 1-3.For example, HVAC subsystem 440 can include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 442 caninclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 438 caninclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4, BMS controller 366 is shown to include acommunications interface 407 and a BMS interface 409. Interface 407 mayfacilitate communications between BMS controller 366 and externalapplications (e.g., monitoring and reporting applications 422,enterprise control applications 426, remote systems and applications444, applications residing on client devices 448, etc.) for allowinguser control, monitoring, and adjustment to BMS controller 366 and/orsubsystems 428. Interface 407 may also facilitate communications betweenBMS controller 366 and client devices 448. BMS interface 409 mayfacilitate communications between BMS controller 366 and buildingsubsystems 428 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 407, 409 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 428 or other external systems or devices. Invarious embodiments, communications via interfaces 407, 409 can bedirect (e.g., local wired or wireless communications) or via acommunications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 407, 409 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 407, 409can include a Wi-Fi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces407, 409 can include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 409 is an Ethernetinterface. In other embodiments, both communications interface 407 andBMS interface 409 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 4, BMS controller 366 is shown to include aprocessing circuit 404 including a processor 406 and memory 408.Processing circuit 404 can be communicably connected to BMS interface409 and/or communications interface 407 such that processing circuit 404and the various components thereof can send and receive data viainterfaces 407, 409. Processor 406 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 408 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 408 can be or include volatile memory ornon-volatile memory. Memory 408 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to someembodiments, memory 408 is communicably connected to processor 406 viaprocessing circuit 404 and includes computer code for executing (e.g.,by processing circuit 404 and/or processor 406) one or more processesdescribed herein.

In some embodiments, BMS controller 366 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 366 can be distributed across multipleservers or computers (e.g., that can exist in distributed locations).Further, while FIG. 4 shows applications 422 and 426 as existing outsideof BMS controller 366, in some embodiments, applications 422 and 426 canbe hosted within BMS controller 366 (e.g., within memory 408).

Still referring to FIG. 4, memory 408 is shown to include an enterpriseintegration layer 410, an automated measurement and validation (AM&V)layer 412, a demand response (DR) layer 414, a fault detection anddiagnostics (FDD) layer 416, an integrated control layer 418, and abuilding subsystem integration later 420. Layers 410-420 can beconfigured to receive inputs from building subsystems 428 and other datasources, determine optimal control actions for building subsystems 428based on the inputs, generate control signals based on the optimalcontrol actions, and provide the generated control signals to buildingsubsystems 428. The following paragraphs describe some of the generalfunctions performed by each of layers 410-420 in BMS 400.

Enterprise integration layer 410 can be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 426 can be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 426 may also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 366. In yet other embodiments, enterprisecontrol applications 426 can work with layers 410-420 to optimizebuilding performance (e.g., efficiency, energy use, comfort, or safety)based on inputs received at interface 407 and/or BMS interface 409.

Building subsystem integration layer 420 can be configured to managecommunications between BMS controller 366 and building subsystems 428.For example, building subsystem integration layer 420 may receive sensordata and input signals from building subsystems 428 and provide outputdata and control signals to building subsystems 428. Building subsystemintegration layer 420 may also be configured to manage communicationsbetween building subsystems 428. Building subsystem integration layer420 translate communications (e.g., sensor data, input signals, outputsignals, etc.) across a plurality of multi-vendor/multi-protocolsystems.

Demand response layer 414 can be configured to optimize resource usage(e.g., electricity use, natural gas use, water use, etc.) and/or themonetary cost of such resource usage in response to satisfy the demandof building 10. The optimization can be based on time-of-use prices,curtailment signals, energy availability, or other data received fromutility providers, distributed energy generation systems 424, fromenergy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or fromother sources. Demand response layer 414 may receive inputs from otherlayers of BMS controller 366 (e.g., building subsystem integration layer420, integrated control layer 418, etc.). The inputs received from otherlayers can include environmental or sensor inputs such as temperature,carbon dioxide levels, relative humidity levels, air quality sensoroutputs, occupancy sensor outputs, room schedules, and the like. Theinputs may also include inputs such as electrical use (e.g., expressedin kWh), thermal load measurements, pricing information, projectedpricing, smoothed pricing, curtailment signals from utilities, and thelike.

According to some embodiments, demand response layer 414 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 418, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 414 may also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 414 may determine to begin using energyfrom energy storage 427 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 414 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which minimize energy costs based on one or moreinputs representative of or based on demand (e.g., price, a curtailmentsignal, a demand level, etc.). In some embodiments, demand responselayer 414 uses equipment models to determine an optimal set of controlactions. The equipment models can include, for example, thermodynamicmodels describing the inputs, outputs, and/or functions performed byvarious sets of building equipment. Equipment models may representcollections of building equipment (e.g., subplants, chiller arrays,etc.) or individual devices (e.g., individual chillers, heaters, pumps,etc.).

Demand response layer 414 may further include or draw upon one or moredemand response policy definitions (e.g., databases, XML, files, etc.).The policy definitions can be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs can be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment can be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 418 can be configured to use the data input oroutput of building subsystem integration layer 420 and/or demandresponse later 414 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 420,integrated control layer 418 can integrate control activities of thesubsystems 428 such that the subsystems 428 behave as a singleintegrated supersystem. In some embodiments, integrated control layer418 includes control logic that uses inputs and outputs from a pluralityof building subsystems to provide greater comfort and energy savingsrelative to the comfort and energy savings that separate subsystemscould provide alone. For example, integrated control layer 418 can beconfigured to use an input from a first subsystem to make anenergy-saving control decision for a second subsystem. Results of thesedecisions can be communicated back to building subsystem integrationlayer 420.

Integrated control layer 418 is shown to be logically below demandresponse layer 414. Integrated control layer 418 can be configured toenhance the effectiveness of demand response layer 414 by enablingbuilding subsystems 428 and their respective control loops to becontrolled in coordination with demand response layer 414. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 418 can be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 418 can be configured to provide feedback todemand response layer 414 so that demand response layer 414 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints may also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer418 is also logically below fault detection and diagnostics layer 416and automated measurement and validation layer 412. Integrated controllayer 418 can be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 412 can be configuredto verify that control strategies commanded by integrated control layer418 or demand response layer 414 are working properly (e.g., using dataaggregated by AM&V layer 412, integrated control layer 418, buildingsubsystem integration layer 420, FDD layer 416, or otherwise). Thecalculations made by AM&V layer 412 can be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 412 may compare a model-predictedoutput with an actual output from building subsystems 428 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 416 can be configured toprovide on-going fault detection for building subsystems 428, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 414 and integrated control layer 418. FDDlayer 416 may receive data inputs from integrated control layer 418,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 416 may automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults can include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 416 can be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 420. In other exemplary embodiments, FDD layer 416 isconfigured to provide “fault” events to integrated control layer 418which executes control strategies and policies in response to thereceived fault events. According to some embodiments, FDD layer 416 (ora policy executed by an integrated control engine or business rulesengine) may shut-down systems or direct control activities around faultydevices or systems to reduce energy waste, extend equipment life, orassure proper control response.

FDD layer 416 can be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer416 may use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 428 may generatetemporal (i.e., time-series) data indicating the performance of BMS 400and the various components thereof. The data generated by buildingsubsystems 428 can include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 416 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Referring now to FIG. 5, a block diagram of another building managementsystem (BMS) 500 is shown, according to some embodiments. BMS 500 can beused to monitor and control the devices of HVAC system 100, watersidesystem 200, airside system 300, building subsystems 428, as well asother types of BMS devices (e.g., lighting equipment, securityequipment, etc.) and/or HVAC equipment.

BMS 500 provides a system architecture that facilitates automaticequipment discovery and equipment model distribution. Equipmentdiscovery can occur on multiple levels of BMS 500 across multipledifferent communications busses (e.g., a system bus 554, zone buses556-560 and 564, sensor/actuator bus 566, etc.) and across multipledifferent communications protocols. In some embodiments, equipmentdiscovery is accomplished using active node tables, which provide statusinformation for devices connected to each communications bus. Forexample, each communications bus can be monitored for new devices bymonitoring the corresponding active node table for new nodes. When a newdevice is detected, BMS 500 can begin interacting with the new device(e.g., sending control signals, using data from the device) without userinteraction.

Some devices in BMS 500 present themselves to the network usingequipment models. An equipment model defines equipment objectattributes, view definitions, schedules, trends, and the associatedBACnet value objects (e.g., analog value, binary value, multistatevalue, etc.) that are used for integration with other systems. Somedevices in BMS 500 store their own equipment models. Other devices inBMS 500 have equipment models stored externally (e.g., within otherdevices). For example, a zone coordinator 508 can store the equipmentmodel for a bypass damper 528. In some embodiments, zone coordinator 508automatically creates the equipment model for bypass damper 528 or otherdevices on zone bus 558. Other zone coordinators can also createequipment models for devices connected to their zone busses. Theequipment model for a device can be created automatically based on thetypes of data points exposed by the device on the zone bus, device type,and/or other device attributes. Several examples of automatic equipmentdiscovery and equipment model distribution are discussed in greaterdetail below.

Still referring to FIG. 5, BMS 500 is shown to include a system manager502; several zone coordinators 506, 508, 510 and 518; and several zonecontrollers 524, 530, 532, 536, 548, and 550. System manager 502 canmonitor data points in BMS 500 and report monitored variables to variousmonitoring and/or control applications. System manager 502 cancommunicate with client devices 504 (e.g., user devices, desktopcomputers, laptop computers, mobile devices, etc.) via a datacommunications link 574 (e.g., BACnet IP, Ethernet, wired or wirelesscommunications, etc.). System manager 502 can provide a user interfaceto client devices 504 via data communications link 574. The userinterface may allow users to monitor and/or control BMS 500 via clientdevices 504.

In some embodiments, system manager 502 is connected with zonecoordinators 506-510 and 518 via a system bus 554. System manager 502can be configured to communicate with zone coordinators 506-510 and 518via system bus 554 using a master-slave token passing (MSTP) protocol orany other communications protocol. System bus 554 can also connectsystem manager 502 with other devices such as a constant volume (CV)rooftop unit (RTU) 512, an input/output module (IOM) 514, a thermostatcontroller 516 (e.g., a TEC5000 series thermostat controller), and anetwork automation engine (NAE) or third-party controller 520. RTU 512can be configured to communicate directly with system manager 502 andcan be connected directly to system bus 554. Other RTUs can communicatewith system manager 502 via an intermediate device. For example, a wiredinput 562 can connect a third-party RTU 542 to thermostat controller516, which connects to system bus 554.

System manager 502 can provide a user interface for any devicecontaining an equipment model. Devices such as zone coordinators 506-510and 518 and thermostat controller 516 can provide their equipment modelsto system manager 502 via system bus 554. In some embodiments, systemmanager 502 automatically creates equipment models for connected devicesthat do not contain an equipment model (e.g., IOM 514, third partycontroller 520, etc.). For example, system manager 502 can create anequipment model for any device that responds to a device tree request.The equipment models created by system manager 502 can be stored withinsystem manager 502. System manager 502 can then provide a user interfacefor devices that do not contain their own equipment models using theequipment models created by system manager 502. In some embodiments,system manager 502 stores a view definition for each type of equipmentconnected via system bus 554 and uses the stored view definition togenerate a user interface for the equipment.

Each zone coordinator 506-510 and 518 can be connected with one or moreof zone controllers 524, 530-532, 536, and 548-550 via zone buses 556,558, 560, and 564. Zone coordinators 506-510 and 518 can communicatewith zone controllers 524, 530-532, 536, and 548-550 via zone busses556-560 and 564 using a MSTP protocol or any other communicationsprotocol. Zone busses 556-560 and 564 can also connect zone coordinators506-510 and 518 with other types of devices such as variable air volume(VAV) RTUs 522 and 540, changeover bypass (COBP) RTUs 526 and 552,bypass dampers 528 and 546, and PEAK controllers 534 and 544.

Zone coordinators 506-510 and 518 can be configured to monitor andcommand various zoning systems. In some embodiments, each zonecoordinator 506-510 and 518 monitors and commands a separate zoningsystem and is connected to the zoning system via a separate zone bus.For example, zone coordinator 506 can be connected to VAV RTU 522 andzone controller 524 via zone bus 556. Zone coordinator 508 can beconnected to COBP RTU 526, bypass damper 528, COBP zone controller 530,and VAV zone controller 532 via zone bus 558. Zone coordinator 510 canbe connected to PEAK controller 534 and VAV zone controller 536 via zonebus 560. Zone coordinator 518 can be connected to PEAK controller 544,bypass damper 546, COBP zone controller 548, and VAV zone controller 550via zone bus 564.

A single model of zone coordinator 506-510 and 518 can be configured tohandle multiple different types of zoning systems (e.g., a VAV zoningsystem, a COBP zoning system, etc.). Each zoning system can include aRTU, one or more zone controllers, and/or a bypass damper. For example,zone coordinators 506 and 510 are shown as Verasys VAV engines (VVEs)connected to VAV RTUs 522 and 540, respectively. Zone coordinator 506 isconnected directly to VAV RTU 522 via zone bus 556, whereas zonecoordinator 510 is connected to a third-party VAV RTU 540 via a wiredinput 568 provided to PEAK controller 534. Zone coordinators 508 and 518are shown as Verasys COBP engines (VCEs) connected to COBP RTUs 526 and552, respectively. Zone coordinator 508 is connected directly to COBPRTU 526 via zone bus 558, whereas zone coordinator 518 is connected to athird-party COBP RTU 552 via a wired input 570 provided to PEAKcontroller 544.

Zone controllers 524, 530-532, 536, and 548-550 can communicate withindividual BMS devices (e.g., sensors, actuators, etc.) viasensor/actuator (SA) busses. For example, VAV zone controller 536 isshown connected to networked sensors 538 via SA bus 566. Zone controller536 can communicate with networked sensors 538 using a MSTP protocol orany other communications protocol. Although only one SA bus 566 is shownin FIG. 5, it should be understood that each zone controller 524,530-532, 536, and 548-550 can be connected to a different SA bus. EachSA bus can connect a zone controller with various sensors (e.g.,temperature sensors, humidity sensors, pressure sensors, light sensors,occupancy sensors, etc.), actuators (e.g., damper actuators, valveactuators, etc.) and/or other types of controllable equipment (e.g.,chillers, heaters, fans, pumps, etc.).

Each zone controller 524, 530-532, 536, and 548-550 can be configured tomonitor and control a different building zone. Zone controllers 524,530-532, 536, and 548-550 can use the inputs and outputs provided viatheir SA busses to monitor and control various building zones. Forexample, a zone controller 536 can use a temperature input received fromnetworked sensors 538 via SA bus 566 (e.g., a measured temperature of abuilding zone) as feedback in a temperature control algorithm. Zonecontrollers 524, 530-532, 536, and 548-550 can use various types ofcontrol algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control a variable state or condition (e.g., temperature, humidity,airflow, lighting, etc.) in or around building 10.

Building Management System with Energy Prediction Model

Referring generally to FIGS. 6-11, systems and methods for operatingbuilding equipment based on energy prediction models (EPMs) are shownand described, according to some embodiments. For building managers (orother users associated with a building) to be able to make appropriatedecisions regarding operation of building equipment, it may be necessaryto provide the building managers with forecasted energy needs severalhours in advance. Based on the forecasted energy needs, buildingmanagers can plan activities to both reduce a carbon footprint of abuilding and to reduce a cost of electricity. In some embodiments,activities are automatically determined based on an EPM.

An EPM can be used to predict energy consumption needs over time. Insome embodiments, EPMs are point forecasts in that the EPMs predict anenergy need at specific points in time. In some embodiments, EPMs areprobabilistic forecasts in that the EPMs can attribute probabilities topossible energy needs at various points in time. In some embodiments,EPMs take on a different formats in terms of generating predictionsregarding energy needs.

Advantageously, an EPM can be generated using small amounts of historicdata. For example, generation of an EPM may be possible with one monthof building energy consumption data. After initial deployment of theEPM, new data may be collected for the building. The EPM can beretrained based on the newly collected data to ensure the EPM accountsfor changes in building characteristics over time. Even if the EPM istrained using larger quantities of historic data (e.g., months' worth ofhistoric data), building characteristics may nonetheless change overtime due to a variety of factors. For example, building characteristicsmay change due to factors such as occupancy changes, equipmentupgrades/maintenance, seasonal changes, changes in occupant preferences(e.g., temperature preferences), etc. As such, the EPM should beretrained to account for building characteristic changes over time.

As described in detail below throughout FIGS. 6-11, the EPM can beretrained to capture recent building characteristics. However, to reduceprocessing requirements for maintaining the EPM, the EPM can beretrained based on various triggers as described below. In particular,EPM performance can be monitored over time to determine how accuratepredictions of the EPM are with regards to the building characteristics.These and other features of operating building equipment based on EPMsare described in detail below.

It should be noted that EPMs can be generated and utilized for othertypes of equipment other than building equipment. For example, EPMs maybe generated and utilized for hospital equipment, electronic equipment,manufacturing equipment, other industrial equipment, etc. In otherwords, EPMs can be applied to various types of equipment that consumeenergy over time.

Referring now to FIG. 6, a block diagram of an energy predictioncontroller 600 is shown, according to some embodiments. In someembodiments, energy prediction controller 600 is a component of BMScontroller 366 as described with reference to FIGS. 3 and 4. Energyprediction controller 600 can be utilized to operate building equipmentto affect variable states or conditions of a building (e.g., building10). Energy prediction controller 600 can generate control decisionsbased on an energy prediction model (EPM) that can predict energyrequirements for a building over time. It should be noted that abuilding is referred to specifically for sake of example. An EPM can begenerated for other types of spaces/places as well. For example, an EPMmay be generated for a space (e.g., a room) within the building, acampus including multiple buildings, etc. In this way, EPMs can be usedto predict energy needs for various sizes and scopes of energy consumingentities.

Energy prediction controller 600 may be hosted in various locations. Forexample, energy prediction controller 600 may be hosted on a localcontroller of a building, on a desktop computer, a mobile device, on acloud computing system, etc. In effect, energy prediction controller 600can be hosted in various locations so long as energy predictioncontroller 600 can communicate with a building of interest and buildingequipment therein.

Energy prediction controller 600 is shown to include a communicationsinterface 608 and a processing circuit 602. Communications interface 608may include wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with various systems, devices, ornetworks. For example, communications interface 608 may include anEthernet card and port for sending and receiving data via anEthernet-based communications network and/or a Wi-Fi transceiver forcommunicating via a wireless communications network. Communicationsinterface 608 may be configured to communicate via local area networksor wide area networks (e.g., the Internet, a building WAN, etc.) and mayuse a variety of communications protocols (e.g., BACnet, IP, LON, etc.).

Communications interface 608 may be a network interface configured tofacilitate electronic data communications between energy predictioncontroller 600 and various external systems or devices (e.g., weatherservice 626, building equipment 628, user device 630, etc.). Forexample, energy prediction controller 600 may provide control signals tobuilding equipment 628 via communications interface 608.

Processing circuit 602 is shown to include a processor 604 and memory606. Processor 604 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 604 maybe configured to execute computer code or instructions stored in memory606 or received from other computer readable media (e.g., CDROM, networkstorage, a remote server, etc.).

Memory 606 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 606 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory606 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 606 may be communicably connected toprocessor 604 via processing circuit 602 and may include computer codefor executing (e.g., by processor 604) one or more processes describedherein. In some embodiments, one or more components of memory 606 arepart of a singular component. However, each component of memory 606 isshown independently for ease of explanation.

Memory 606 is shown to include a data collector 610. Data collector 610can be configured to collect data from a variety of sources viacommunications interface 608. For example, data collector 610 maycollect/receive weather data from a weather service 626, operationaldata from building equipment 628, user inputs from a user device 630,etc. It should be appreciated that in FIG. 6, weather service 626,building equipment 628, and user device 630 are provided as examples ofpossible sources of data. Data collector 610 may collect data from anysource that has information applicable to energy prediction, buildingequipment control, etc.

Energy prediction controller 600 is shown to receive weather data fromweather service 626. Weather service 626 can include a variety ofsources of weather information. For example, weather service 626 mayinclude a weather application programming interface (API) accessible byenergy prediction controller 600, a local weather station that transmitsweather information to energy prediction controller 600, weather sensorsthat measure outdoor weather conditions (e.g., near a building ofinterest), etc. Weather data provided by weather service 626 may includeinformation such as an outdoor temperature, a humidity level, an airquality level, a cloud-cover ratio, etc. The weather data may includecurrent weather data and/or predicted weather data over an upcoming timeperiod.

Energy prediction controller 600 is shown to receive operational datafrom building equipment 628. Building equipment 628 can includeequipment that operates to affect variables states or conditions in abuilding. In particular, building equipment 628 may operate to affectenvironmental conditions in a building. As defined herein, environmentalconditions can include any conditions in a building that may affect anoverall state of the building. For example, environmental conditions mayinclude temperature, humidity, air quality, lighting, security, airpressure, etc. As such, building equipment 628 may include boilers,humidifiers, air purifiers, chillers, other heating, ventilation, or airconditioning (HVAC) equipment, security systems (e.g., locks, alarms,barriers), lighting equipment, etc. that can affect environmentalconditions. In some embodiments, building equipment 628 includes someand/or all of the subsystems of building subsystems 428 as describedwith reference to FIG. 4.

Still referring to FIG. 6, energy prediction controller 600 is alsoshown to receive user inputs from user device 630. User inputs caninclude instructions/information provided by users via user device 630.For example, user inputs may include equipment servicing/maintenanceschedules, indications of changes to building equipment 628 (e.g.,additions or removals of building equipment 628), changes in operationalprocedures (e.g., changes in heating/cooling strategies), changes indata collection capabilities (e.g., information regarding new energymeters), manual estimations of energy needs over time, changes inbuilding characteristics, etc.

User device 630 may be one or more personal computing devices associatedwith a user or occupant of the building. For example, user device 630may be utilized by a building operator of the building. User device 630may include any wearable or non-wearable device. Wearable devices canrefer to any type of device that an individual wears including, but notlimited to, a watch (e.g., a smart watch), glasses (e.g., smartglasses), bracelet (e.g., a smart bracelet), etc. User device 630 mayalso include any type of mobile device including, but not limited to, aphone (e.g., smart phone), a tablet, a personal digital assistant, etc.In some embodiments, user device 630 includes other computing devicessuch as a desktop computer, a laptop computer, etc. The user device 630is configured to display a graphical user interface to a user andreceive user input to the graphical user interface. In some embodiments,user device 630 includes a touchscreen. User device 630 may becommunicable with the energy prediction controller 600 via a network,for example a WiFi network, a Bluetooth network, a cellular network,etc.

Based on received data (e.g., weather data, operational data, userinputs, etc.), data collector 610 can compile and/or provide the data toother components of memory 606. In particular, data collector 610 canprovide collected data to an external factor monitor 612 and a datapre-processor 614. In some embodiments, data collector 610 provides allreceived information to external factor monitor 612 and/or datapre-processor 614. In some embodiments, data collector 610 selectsspecific information to provide to external factor monitor 612 and/ordata pre-processor 614. In particular, data collector 610 may determinedata requirements of external factor monitor 612 and/or datapre-processor 614 and selectively provide data based on the datarequirements. In some embodiments, data collector 610 stores anyreceived information in a database(s) for later retrieval and access.Said database(s) (not shown) may be a component of energy predictioncontroller 600 and/or may an external database that energy predictioncontroller 600 can communicate with. For example, data collector 610 maystore collected information on a cloud database hosted by a cloudprovider.

Memory 606 is shown to include external factor monitor 612. Externalfactor monitor 612 can track various data points to determine if an EPMrequires retraining (or generation if no EPM exists). In other words,external factor monitor 612 may track statuses of triggering events asindicated by the collected data provided by data collector 610 anddetermine if any triggering event has occurred. Examples of triggeringevents can include a certain period of time passing since a last modelretraining, indications by users that the EPM should be retrained,changes to qualities of input data to the EPM, etc. Triggering eventsand external factor monitor 612 are described in greater detail belowwith reference to FIG. 7

If external factor monitor 612 determines that a certain triggeringevent has occurred, external factor monitor 612 may generate a triggermessage to provide the trigger message to a dynamic hyper-parametersearch (DHPS) module 618. Based on the trigger message, DHPS module 618can initiate a dynamic hyper-parameter search which is described ingreater detail below. In this way, external factor monitor 612 caninitiate the dynamic hyper-parameter search based on external factors.In other words, external factor monitor 612 can initiate the dynamichyper-parameter search based on factors other than based on performanceof a current EPM.

Memory 606 is also shown to include a data pre-processor 614. Datapre-processor 614 can collect data since a last EPM retraining andperform various pre-processing on said data. For example, if a lastretraining of the EPM occurred at the end of January and a current timeis the end of February, data pre-processor 614 can access all datagathered over the course of the month of February. Data pre-processingperformed by data pre-processor 614 can include operations to cleandata, purge unnecessary data, and/or other operations to increase ausefulness of data provided to a performance metric calculator 616and/or DHPS module 618. It should be noted that data pre-processor 614can also choose to utilize historic data prior to a last modelretraining, such as prior to end-of-January in this example, to jointlypre-process old and new data.

Memory 606 is shown to include performance metric calculator 616.Performance metric calculator 616 can monitor EPM performance over timeto determine if EPM performance is within acceptable bounds. In someembodiments, the acceptable bounds are automatically set by performancemetric calculator 616 and/or another component of energy predictioncontroller 600. In some embodiments, the acceptable bounds are set by anexternal entity such as a user via user device 630. Performance metriccalculator 616 as well as performance metrics that can be calculated andtracked by performance metric calculator 616 are described in greaterdetail below with reference to FIG. 8.

If performance metric calculator 616 determines the EPM is in violationof acceptable performance bounds, performance metric calculator 616 cangenerate and provide a trigger message to DHPS module 618. In this way,performance metric calculator 616 may include some of the functionalityof external factor monitor 612. In some embodiments, performance metriccalculator 616 and external factor monitor 612 are part of a singlecomponent of memory 606 for monitoring variables andgenerating/providing trigger messages accordingly.

If at least one of external factor monitor 612 or performance metriccalculator 616 generate and provide a trigger message to DHPS module618, DHPS module 618 can initiate a dynamic hyper-parameter search.However, if both external factor monitor 612 and performance metriccalculator 616 determine no model retraining is necessary (i.e., notrigger messages are provided to DHPS module 618), external factormonitor 612 and performance metric calculator 616 can continuemonitoring variables over time. In this way, external factor monitor 612and/or performance metric calculator 616 may eventually detect a modelretraining is necessary and trigger DHPS module 618 in response.

In some embodiments, external factor monitor 612 and/or performancemetric calculator 616 continuously monitor variables to determine ifmodel retraining is necessary. In this case, model retraining may beinitiated quickly after the retraining is necessary to be performed. Insome embodiments, external factor monitor 612 and/or performance metriccalculator 616 may periodically and/or occasionally determine if modelretraining is required. In this case, processing requirements formonitoring variables may be decreased. However, a time delay betweenwhen the model retraining is needed and when the need is detected may belonger than if the variables are monitored continuously.

Still referring to FIG. 6, memory 606 is shown to include DHPS module618. In response to receiving a trigger message, DHPS module 618 caninitiate a DHPS. A DHPS can include performing one or more optimizationsto determine an optimal set of hyper-parameters for the EPM. It shouldbe noted that the term “optimal” as used herein may or may not indicatean ideal result. As used herein, “optimal” may indicate a best solutionof available/determined solutions. In other words, an ideal solution(i.e., a perfect solution) may or may not be identified dependent on asearch/optimization strategy utilized. Specifically with regards to theDHPS performed by DHPS module 618, the DHPS may not search everypossible hyper-parameter combination, and as such, may or may not find a“perfect” combination, that is, a global optimum.

As defined herein, a hyper-parameter of the EPM can include anyparameter of the EPM whose initial value is set before a learningprocess and/or retraining process of the EPM begins. In this way,initial values of hyper-parameters may be known prior to the learningprocess and/or the retraining process. Examples of hyper-parameters mayinclude, but are not limited to, a training window parameter, a transferlearning flag, a training epoch, a training batch size, a lag parameter(i.e., a number of sampling intervals of data that forms ‘x’ of trainingsamples), model architecture parameters (e.g., a number of neuralnetwork layers, a hidden layer size if a long-short term memory (LSTM)model is used, a number of stack of LSTMs, etc.), and/or otherappropriate parameters. Specifically, the transfer learning flag mayindicate if model weights will be initialized with weights of acurrently deployed model. The training window parameter can indicate aproportion of old and new data to use that carries useful informationfor modeling.

To perform the DHPS, DHPS module 618 can perform an optimization processto test various combinations of hyper-parameters to determination whatcombination of hyper-parameters results in the best energy requirementpredictions. DHPS module 618 can utilize processed data provided by datapre-processor 614 in analyzing hyper-parameter combinations.Specifically, for each hyper-parameter combination analyzed, DHPS module618 can evaluate a relative performance of each combination anddetermine what combination provides the best performance.

As it may be computationally inefficient to test every combination ofhyper-parameters, DHPS module 618 may select specific combinations foranalysis based on certain criteria. For example, DHPS module 618 maydetermine hyper-parameters of an EPM currently in use and analyzecombinations of hyper-parameters similar to that to the EPM currently inuse. This strategy intuitively makes sense as the EPM currently in usewas the optimal model of a previous retraining process, and thereforehas hyper-parameters that may be relatively appropriate. As anotherexample, DHPS module 618 may eliminate and/or hone in on certaincombinations based on other tested combinations. In this case, if acertain combination of hyper-parameters results in extremely poorperformance, similar combinations may likewise be eliminated fromconsideration, thereby reducing a number of tested combinations.Alternatively, if a certain combination results in extremely highperformance, similar combinations can likewise be tested, therebyfocusing the search on a certain range of hyper-parameter values. Itshould be noted that for each hyper-parameter combination tested by DHPSmodule 618, a dimensionality of the combinations may remain constant. Inother words, DHPS module 618 may maintain the same number ofhyper-parameters in between each combination tested, and only changeactual values of the hyper-parameters themselves. DHPS module 618 andthe DHPS are described in greater detail below with reference to FIG. 9.

Based on the DHPS, an optimal combination of hyper-parameters for theEPM can be automatically and/or dynamically selected. The selectedcombination of hyper-parameters can be integrated into the EPM by DHPSmodule 618. Afterwards, DHPS module 618 can provide the EPM to EPMretraining module 620. In some embodiments, DHPS module 618 provides theselected combination of hyper-parameters and provides said combinationto EPM retraining module 620 such that EPM retraining module 620 canintegrate the combination into the EPM. It should be appreciated that byselecting a specific combination of hyper-parameters, the EPM is notonly retrained, but specific parameters of the EPM can be selectivelyupdated to improve performance of the EPM. In other words, qualities ofthe EPM are changing as opposed to just training data used to train theEPM. Examples of qualities may include, but are not limited to, anarchitecture of the EPM, how far into the future the EPM is designed topredict, etc.

Memory 606 is shown to include EPM retraining module 620. EPM retrainingmodule 620 can perform a model retraining process to update the EPM. EPMretraining module 620 can utilize training data collected by datacollector 610 to retrain the EPM. EPM retraining module 620 can utilizeboth old training data and new training data to retrain the EPM.Specifically, EPM retraining module 620 may train the EPM respective toa training window parameter optimized by DHPS module 618 that indicatesa proportion of old and new data to utilize. For example, the trainingwindow parameter may define the proportion such that the EPM isretrained using 80% new data and 20% old data. As another example, thetraining window parameter may define the proportion such that the EPM isretrained using 50% old data and 50% new data. In some embodiments, theproportion is instead defined by a user, automatically by EPM retrainingmodule 620, etc. For example, a user may define a proportion thatoverrides the training window parameter. In some embodiments, thetraining window parameter also defines a total length of training data(i.e., a number of training samples), in addition to the proportion ofnew and old data. Differences between old and new data can be definedbased on a variety of qualifiers. For example, new data may be definedby any data collected since a last model retraining whereas old data maybe defined by data collected prior to the last model retraining. Asanother example, old and new data may be defined based on an amount oftime since the data was collected (e.g., old data can be defined as datagathered over a month ago). By utilizing data respective of the trainingwindow, a quality of retraining of the EPM can be improved. To retrainthe EPM, EPM retraining module 620 may initiate a learning process toretrain how the EPM associates certain inputs to certain outputs. In thelearning process, parameters of the EPM can be updated. However, in thelearning process, the hyper-parameters set by DHPS module 618 may remainuntouched. In this way, a quality of the retrained EPM can be maximizedrespective to available data.

Still referring to FIG. 6, memory 606 is shown to include a modelselector 622. Model selector 622 can perform a model selection processto identify a best available model to be used for controlling buildingequipment 628. As DHPS module 618 may not perform an exhaustive searchof all hyper-parameter combinations, a newly retrained EPM may or maynot be better than an existing EPM (i.e., a currently deployed EPM). Assuch, model selector 622 can compare the newly retrained EPM with theexisting EPM to determine which model provides better overallperformance and quality of energy need predictions. In this case, thenewly retrained EPM may only replace the existing EPM if the newlyretrained EPM results in better performance. To compare the models,model selector 622 may pass data on a recent test window through eachEPM and determine which EPM results in more accurate predictions.

In some embodiments, model selector 622 compares multiple EPMs beyondthe newly retrained EPM and the existing EPM. For example, modelselector 622 may store seasonal EPMs that are utilized during specificseasons. In this case, model selector 622 may have a “winter” EPMspecific to building characteristics in winter, a “summer” EPM specificto building characteristics in summer, etc. During comparisons, modelselector 622 may compare the newly retrained EPM, the existing EPM, andan EPM associated with a current season to determine a most accuratemodel. Comparing predictions of multiple models can ensure that betterEPMs are utilized over time.

Model selector 622 can provide a selected EPM to an equipment controller624. Equipment controller 624 can utilize the selected EPM to generatecontrol signals to operate building equipment 628. By utilizing theselected EPM, equipment controller 624 can forecast energy needs intothe future and plan activities to reduce a carbon footprint and a totalcost of electricity over time. For example, equipment controller 624 mayutilize the selected EPM to determine required loads of the building andgenerate control signals that operate building equipment 628 duringnon-peak demand charge times to reduce an overall cost of electricitywhilst still fulfilling the load demands of the building.

In some embodiments, model selector 622 provides the selected EPM to adevice/system external to energy prediction controller 600. For example,model selector 622 may provide the selected EPM to user device 630(e.g., via communications interface 608) such that a user (e.g., abuilding owner) can plan activities accordingly. As another example,model selector 622 may provide the selected EPM to a separate controllerthat operates building equipment (e.g., a model predictive controlcontroller). In essence, model selector 622 can provide the selected EPMto any device, component, system, etc. internal to or separate fromenergy prediction controller 600.

Referring now to FIG. 7, a block diagram of external factor monitor 612in greater detail is shown, according to some embodiments. Thecomponents of external factor monitor 612 shown in FIG. 7 are given forsake of example. External factor monitor 612 may include less,different, and/or additional components as compared to what is shown inFIG. 7. Components of external factor monitor 612 can utilize collecteddata provided by data collector 610 in determining whether a DHPS and amodel retraining process should be initiated.

External factor monitor 612 is shown to include a trigger generator 702.Trigger generator 702 can generate triggers messages that can beprovided to DHPS module 618. Trigger generator 702 can generate triggersmessages in response to indications by other components of externalfactor monitor 612. For example, a model monitor 704 of external factormonitor 612, described in detail below, may indicate to triggergenerator 702 that no EPM exists and therefore a new EPM should begenerated. Based on the indication, trigger generator 702 can generate atrigger message and provide the trigger message to DHPS module 618.

In some embodiments, trigger generator 702 is configured to generate atrigger message after a certain amount of time such that a retrainingprocess is performed periodically. In this way, an EPM can be retrainedregardless of whether bounds of other variables are violated.Periodically updating the EPM can ensure that the EPM that the EPM isreflective of newly obtained data.

In some embodiments, external factor monitor 612 includes model monitor704. Model monitor 704 can track whether or not an EPM exists and/orwhether an EPM is being utilized. If no EPM exists, model monitor 704may provide an indication to trigger generator 702 to generate andtransmit a trigger message. Likewise, if model monitor 704 determinesthat an EPM exists but is not being utilized (e.g., the EPM was disabledfrom use by a user), model monitor 704 may provide the indication totrigger generator 702.

In some embodiments, external factor monitor 612 includes a notificationmonitor 706. Notification monitor 706 can analyze collected dataprovided by data collector 610 to determine if any user inputs areprovided that may indicate updating of the EPM is required. In otherwords, notification monitor 706 can determine if any changes to buildingcharacteristics are indicated in user inputs. For example, notificationmonitor 706 may analyze the collected data to detectservicing/maintenance notifications for building equipment,notifications regarding replacement of building equipment, changes inoperational procedures (e.g., new heating/cooling strategies), changesin how data is collected (e.g., new energy meters or other instrumentsare installed), an explicit request by a user to update the EPM, etc. Inthis way, user inputs can be used to determine if an update to the EPMis required.

In some embodiments, external factor monitor 612 includes a predictorvariable monitor 708. Predictor variable monitor 708 can analyze thecollected information provided by data collector 610 to determinewhether any new predictor variables are available. Predictor variablescan include variables that can be used to refine energy needpredictions. For example, predictor variable monitor 708 may identifythat an occupancy count has become available as a predictor variable.The occupancy count can provide insights into how energy needs of thebuilding may change over time due to an amount of people in thebuilding. As such, the EPM can be updated to reflect the occupancycount. Therefore, responsive to a detection of a new predictor variable,predictor variable monitor 708 can indicate to trigger generator 702 togenerate and provide a new trigger message to DHPS module 618.

In some embodiments, external factor monitor 612 includes an inputmonitor 710. Input monitor 710 can track various input variablesutilized by the EPM for quality changes. For example, the EPM mayutilize a weather forecast provided by weather service 626. If inputmonitor 710 determines that a quality of the weather forecast hassignificantly degraded, input monitor 710 may determine that the EPMshould be updated. As another example, input monitor 710 may trackoccupant-initiated setpoints for environmental conditions such astemperature. If the occupant-initiated setpoints change significantly(e.g., an occupant raises a temperature setpoint by 5 degreesFahrenheit), input monitor 710 may determine occupant preferences havechanged and that the EPM should be updated accordingly. As such, inputmonitor 710 can indicate to trigger generator 702 to generate andprovide a new trigger message to DHPS module 618.

In some embodiments, external factor monitor 612 includes a consumingapplication monitor 712. Consuming application monitor 712 can trackchanges in requirements of an application that utilizes (consumes)energy predictions of the EPM. For example, equipment controller 624 mayinclude a model predictive control (MPC) application that utilizes theenergy predictions. If the MPC application switches to requiring energypredictions at 15 minute sampling rates from hourly sampling rates,consuming application monitor 712 may determine the EPM should beupdated. As another example, if the MPC switches to requiringpredictions 48 hours into the future from 24 hours ahead, consumingapplication monitor 712 may determine the EPM should be updated.Responsive to a determination that the EPM should be updated, consumingapplication monitor 712 can indicate to trigger generator 702 togenerate and provide a new trigger message to DHPS module 618.

As mentioned above, the components of external factor monitor 612 aregiven purely for sake of example. External factor monitor 612 mayinclude any appropriate components for analyzing external factors thatmay necessitate an update of the EPM.

Referring now to FIG. 8, a block diagram of performance metriccalculator 616 in greater detail is shown, according to someembodiments. As with external factor monitor 612 as described withreference to FIG. 7, components of performance metric calculator 616 asshown in FIG. 8 are given purely for sake of example. Performance metriccalculator 616 can include any appropriate component for monitoring oneor more performance metrics of the EPM.

Performance metric calculator 616 is shown to include a triggergenerator 802. In some embodiments, trigger generator 802 is similar toand/or the same as trigger generator 702 of external factor monitor 612as described with reference to FIG. 7. In particular, trigger generator802 can generate and provide a trigger message to DHPS module 618responsive to a component of performance metric calculator 616determining an update of the EPM may be required.

Performance metric calculator 616 is shown to include a coefficient ofvariation root mean squared error (CV-RMSE) calculator 804. CV-RMSEcalculator 804 can calculate a CV-RMSE over a time period (e.g., over ndays). To calculate the CV-RMSE, CV-RMSE calculator 804 may firstcalculated a root mean squared error (RMSE) for the last n days (or someother time period) based on the following equation:

${RMSE} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{n}\left( {x_{i} - {\hat{x}}_{i}} \right)^{2}}}$where x_(i) indicates an actual energy required and {circumflex over(x)}_(i) indicates a predicted energy based on the EPM. Based on theRMSE, CV-RMSE calculator 804 can calculate the CV-RMSE using thefollowing equation:

${cv}_{rmse} = {\frac{RMSE}{\frac{1}{N}{\sum\limits_{i = 1}^{n}\left( x_{i} \right)}} \times 100\%}$where cv_(rmse) is the CV-RMSE.

If the CV-RMSE is calculated, CV-RMSE calculator 804 can compare theCV-RMSE with a threshold on CV-RMSE T_(cv,rmse). In particular, CV-RMSEcalculator 804 can determine that the EPM should be updated responsiveto determining thatcv _(rmse)[t]>T _(cv,rmse)where cv_(rsme)[t] is the CV-RMSE at retraining instance t. In thiscase, a retraining instance may refer to a time instance whenperformance metric calculator 616 calculates various metrics such asCV-RMSE.

Performance metric calculator 616 is also shown to include a cumulativesum (CUSUM) calculator 806. CUSUM calculator 806 can calculate a CUSUMof a CV-RMSE differential and determine if said CUSUM is acceptablerelative to some threshold value(s). To calculate the CUSUM of theCV-RMSE differential, CUSUM calculator 806 can utilize the followingequations:Δcv _(rmse)[t]=cv _(rmse)[t]−cv _(rmse)[t−1]S[t]=max(0,S[t−1]+Δcv _(rmse)[t])where cv_(rsme)[t] is the CV-RMSE at current retraining instance t asdefined above, Δcv_(rsme) [t] is the CV-RMSE differential at instance t,and S[t] is the CUSUM of CV-RMSE differential at instance t.

If the CUSUM of CV-RMSE differential is calculated, CUSUM calculator 806can compare the CUSUM of CV-RMSE differential to a threshold on CUSUMT_(s) to determine if the EPM should be updated. In particular, CUSUMcalculator 806 may determine that EPM should be updated responsive todetermining that S[t]>T_(s).

Performance metric calculator 616 is also shown to include a percentilecalculator 808. Percentile calculator 808 can calculate a percentile ofCV-RMSE as averaged over the last n days (or some other time period).Percentile calculator 808 can calculate the percentile of CV-RMSE over adistribution of CV-RMSE calculated in past retraining epochs. Tocalculate the percentile of CV-RMSE, percentile calculator 808 canutilize the following equations:cdf(cv _(rmse)[τ]),τ∈{t,t−1, . . . ,t−M}ν=cdf(cv _(rmse)[t])where cdf(cv_(rsme)[τ]) is a cumulative distribution function (CDF) ofCV-RMSE calculated over a past M retraining instances andν=cdf(cv_(rsme)[t]) is a CDF of CV-RMSE calculated in a currentretraining instance. If percentile calculator 808 determines thepercentile of CV-RMSE is not in an adequate percentile, percentilecalculator 808 can indicate to trigger generator 802 that the EPM shouldbe updated.

If the percentile of CV-RMSE is calculated, percentile calculator 808can compare the percentile with a threshold on percentile. For example,if CV-RMSE is over a 90^(th) percentile of previously observed CV-RMSE,percentile calculator 808 may determine an update to the EPM should betriggered. In particular, can determine if:ν=cdf(cv _(rmse)[t])>T _(cdf)where T_(cdf) is the threshold on the percentile. Advantageously,comparing the CV-RMSE to previously observed CV-RMSEs can ensure thatthe threshold on percentile is relative and is based on the building'spredictability and not on unrealistic absolute metrics. In other words,a specific threshold may not be required to be selected. Rather, thethreshold can be more dynamic to account for changing buildingcharacteristics. As should be appreciated, this can provide additionalconfidence in determining whether the EPM should be updated.

Referring now to FIG. 9, a block diagram of DHPS module 618 in greaterdetail is shown, according to some embodiments. As with external factormonitor 612 and performance metric calculator 616 as described withreference to FIGS. 6 and 7, the components of DHPS module 618 in FIG. 9are provided for sake of example. DHPS module 618 can include anyappropriate components for determining optimal values ofhyper-parameters of an EPM.

DHPS module 618 is shown to include a hyper-parameter definer 902.Hyper-parameter definer 902 can define hyper-parameters to be includedin the EPM. Hyper-parameter definer 902 can define hyper-parametersacross various dimensions. For example, hyper-parameter definer 902 maydefine hyper-parameters including, a transfer learning flag, a trainingwindow, a training epoch, a training batch size, a lag parameter, anumber of neural network layers, a hidden size (e.g., if an LSTM isutilized), a number of stacks of LSTMs, etc. Hyper-parameters defined byhyper-parameter definer 902 can be used in testing various combinationsof hyper-parameters for the EPM.

DHPS module 618 is also shown to include a Bayesian optimizer 904, agradient-based optimizer 906, and an evolutionary optimizer 908.Accordingly, Bayesian optimizer 904 can perform a Bayesian optimization,gradient-based optimizer 906 can perform a gradient-based optimization,and evolutionary optimizer 908 can perform an evolutionary optimization.Each of these optimizations is given for sake of example. Otheroptimizations can be used in determining optimal values ofhyper-parameters of the EPM.

In some embodiments, DHPS module 618 utilizes one of optimizers 904-908to determine optimal values of hyper-parameters. In this case, output ofthe specific optimization performed can be used as values of thehyper-parameters. In some embodiments, multiple optimizations areperformed. For example, each of optimizers 904-908 may perform anassociated optimization. If multiple optimizations are performed, DHPSmodule 618 may compare results of each optimization and determine whatcombination of hyper-parameters is most appropriate. In this way,multiple optimization strategies can be applied to improve quality ofthe hyper-parameters of the EPM.

In some embodiments, DHPS module 618 performs other search processes todetermine an optimal combination of hyper-parameter values. For example,DHPS module 618 may perform an exhaustive search of all hyper-parametervalue combinations. While an exhaustive search may be morecomputationally intensive as compared to optimizations performed byoptimizers 904-908, the exhaustive search may ensure ideal values of thehyper-parameters are identified. This can be particularly useful forbuildings in which building characteristics do not change often, and assuch, a single EPM may be utilized for a long time frame. Therefore,ensuring quality of the EPM may be worth computational expenditure inidentifying hyper-parameter values if DHPS is invoked only occasionally.

Referring now to FIG. 10, a block diagram of model selector 622 ingreater detail is shown, according to some embodiments. Similar toexternal factor monitor 612, performance metric calculator 616, and DHPSmodule 618 as described with reference to FIGS. 7-9, components of modelselector 622 are shown purely for sake of example. Model selector 622can include any appropriate model for selecting an EPM to be utilized inenergy predictions.

It should be noted that the comparisons described below are described asbetween a newly constructed EPM (i.e., a model with hyper-parametersupdated by DHPS module 618) and an existing EPM. However, determinationsregarding what model to use as performed by components of model selector622 can be performed based on more than two models, according to someembodiments. For example, comparisons may be performed between the newlyconstructed EPM, the existing EPM, and a seasonal EPM to determine amost appropriate model. As such, comparisons between two EPMs should notbe construed as limiting on the present disclosure.

Model selector 622 is shown to include a CV-RMSE comparator 1002. Basedon a model with optimized hyper-parameters defined by DHPS module 618,CV-RMSE comparator 1002 can compare a metric (M) of CV-RMSE on the newlyconstructed model with that of an existing model on a recent test windowof data. In particular, CV-RMSE comparator 1002 can compare values ofthe newly constructed EPM and existing EPM to determine if the existingEPM has a higher CV-RMSE as compared to the newly constructed EPM. Ifso, CV-RMSE comparator 1002 may determine that the existing model shouldbe replaced by the newly constructed EPM. Said determination can bemathematically represented as:cv _(rsme) ^(new_model)[t]<cv _(rmse) ^(old_model)[t]where cv_(rsme) ^(new_model)[t] is a CV-RMSE value of the newlyconstructed EPM at time instance t and cv_(rsme) ^(old_model)[t] is aCV-RMSE value of the existing EPM at time instance t. In essence,whatever EPM has a lower CV-RMSE can be deployed.

Model selector 622 is also shown to include a CV-RMSE distributioncomparator 1004. To determine what EPM to utilize, CV-RMSE distributioncomparator 1004 can calculate a distribution of daily CV-RMSE valuesover a test window for both the newly constructed EPM and the existingEPM. Further, CV-RMSE distribution comparator 1004 can calculate ameasure (e.g., a function of mean, standard deviation, number ofsamples) of CV-RMSE and pick a model with a lowest measure. Inparticular, using standard deviation and number of samples can balancelow CV-RMSE with reliability of CV-RMSE.

Mathematically, CV-RMSE distribution comparator 1004 can define thefollowing variables:cv _(rmse)[t]=mean([cv _(rmse)[t,1],cv _(rmse)[t,2], . . . ,cv_(rmse)[t,D])where cv_(rmse)[t, d] is the CV-RMSE calculated per-day on day d ofcurrent retraining instance t and D is a number of test window days forretraining instance t. Further, CV-RMSE distribution comparator 1004 candefine a reliability index as:

${\rho\left( {{cv}_{rmse}\lbrack t\rbrack} \right)} = \frac{D}{\sigma\left( {{cv}_{rmse}\lbrack t\rbrack} \right)}$where σ(cv_(rmse)[t]) is a standard deviation of CV-RMSE samples[cv_(rmse)[t,1], cv_(rmse)[t,2], . . . , cv_(rmse)[t,D]]. In this case,CV-RMSE distribution comparator 1004 can replace the existing EPM withthe newly constructed EPM if:cv _(rmse) ^(new_model)[t]<cv _(rmse) ^(old_model)[t]ANDρ(cv _(rmse) ^(new_model)[t])>T _(reliability)where T_(reliability) is a threshold on reliability.

Model selector 622 is also shown to include a model metric comparator1006. Model metric comparator 1006 can utilize both the newlyconstructed EPM and the existing EPM and can switch between them asneeded to improve quality of energy predictions. In particular, modelselector 622 can monitor a metric M between the hyper-parameteroptimized EPM, the currently deployed EPM (which may or may not be thesame as the hyper-parameter optimized EPM), and the existing EPM. Modelmetric comparator 1006 can track M for each model and can switch thecurrently deployed model out with previously deployed model in the casethat M improves for the previously deployed model beyond the currentlydeployed model. Keeping the previously deployed model can ensurereversal to the old model can occur if the currently deployed modeldeteriorates in prediction accuracy. Said deterioration may occur, forexample, if a temporary shift in building characteristics lead to themodel update and deployment of the hyper-parameter optimized model.

Referring now to FIG. 11, a flow diagram of a process 1100 for deployingan EPM is shown, according to some embodiments. In some embodiments,some and/or all steps of process 1100 are performed by energy predictioncontroller 600. In this way, process 1100 can result in a dynamic,automatic pipeline for updating and deploying EPMs for energypredictions.

Process 1100 is shown to include monitoring external factors and anamount of days since a last retraining of an energy prediction model(EPM) occurred (step 1102). If the amount of days since the lastretraining exceeds a threshold amount of days, a force retrainingvariable may be automatically set to true. Likewise, if any externalfactors (e.g., no model exists, a building manager notifies of a change,new predictor variables are available, external input quality changes,consuming application requirements change, etc.) are determined to haveinitiated a triggering event, the force retraining variable may beautomatically set to true as well. In some embodiments, step 1102 isperformed by external factor monitor 612.

Process 1100 is shown to include performing data pre-processing based ondata gathered since the last retraining (step 1104). The pre-processingof data performed in step 1104 can include various operations on thedata such as organizing the data, cleaning the data (e.g., merging thedata into a common format), purging unnecessary data, etc. In essence,step 1104 can include any operations to improve overall quality of thedata and information that can be extracted therefrom. In someembodiments, step 1104 is performed by data pre-processor 614.

Process 1100 is shown to include determining if a force retraining isrequired (step 1106). In effect, step 1106 can include determining ifthe EPM should be updated. In some embodiments, the determination instep 1106 is based on whether the force retraining variable was set totrue in step 1102. If the force retraining is required (step 1106,“YES”), process 1100 can proceed to step 1112. If the force retrainingis not required (step 1106, “NO”), process 1100 can proceed to step1108. In some embodiments, step 1106 is performed by external factormonitor 612 and/or DHPS module 618.

Process 1100 is shown to include calculating a performance metric of theEPM to determine if the performance metric is within an acceptable range(step 1108). In some embodiments, step 1108 includes calculatingmultiple performance metrics and determining whether each is within anacceptable range. In some embodiments, step 1108 involves calculatingvarious information based on a CV-RMSE value. For example, step 1108 mayinclude calculating a CV-RMSE, a CUSUM of a CV-RMSE differential, apercentile of CV-RMSE as averaged over the last n days, etc. In someembodiments, step 1108 is performed by performance metric calculator616.

Process 1100 is shown to include determining if the performance metricis acceptable (step 1110). The determination in step 1110 can be basedon the calculation in step 1108. If the performance metric is acceptable(step 1110, “YES”), process 1100 can repeat beginning at step 1104. Inthis case, as behavior of the EPM is determined to be suitable and noforce retraining is required, process 1100 may not requiring modelupdating/retraining. If the performance metric is unacceptable (step1110, “NO”), process 1100 may proceed to step 1112.

Process 1100 is shown to include performing a dynamic hyper-parametersearch (step 1112). In step 1112, optimal values of hyper-parameters ofthe EPM can be identified. In particular, step 1112 may includecomparing various combinations of values of the hyper-parameters todetermine a combination that results in the most accurate energypredictions. Each comparison may maintain a constant dimensionalitybetween combinations. In other words, each comparison may change valuesof the hyper-parameters, but keep the same number of hyper-parametersbetween each combination. In some embodiments, step 1112 includesperforming one or more optimizations (e.g., a Bayesian optimization, agradient-based optimization, an evolutionary optimization, etc.) toidentify an optimal combination of hyper-parameter values. In someembodiments, step 1112 includes discarding some collected dataassociated with the EPM. Specifically, step 1112 may include discardingold data as the old data may not be as accurate as more recent data. Insome embodiments, step 1112 is performed by DHPS module 618.

Process 1100 is shown to include retraining the EPM based on the dynamichyper-parameter search (step 1114). As a result of the DHPS performed instep 1112, an optimal combination of hyper-parameter values can beidentified. Said combination can be populated in the EPM such that,prior to retraining the EPM, the hyper-parameter values are set. In thisway, when retraining the EPM, other variables of the EPM may be adjustedto account for updated data, but the hyper-parameters can be constant.In this sense, step 1114 does not just include retraining the EPM, butalso updating actual parameters of the EPM. In other words, the EPMitself is changing rather than just the training data. In someembodiments, step 1114 is performed by EPM retraining module 620.

Process 1100 is shown to include comparing the retrained EPM with anexisting EPM to determine an optimal EPM (step 1116). It should be notedthat, in some embodiments, step 1116 includes comparing additional EPMs(e.g., seasonal EPMs) to the retrained EPM and the existing EPM. Step1116 can include performing one or more comparisons based on variousmetrics of the EPMs. For example, step 1116 may include comparingCV-RMSE values of the EPMs, standard deviations of the CV-RMSEs, etc. Ineffect, based on the comparison(s), the optimal EPM can be identified.In some embodiments, step 1116 is performed by model selector 622.

Process 1100 is shown to include deploying the optimal EPM (step 1118).Deployment of the optimal EPM can include various actions that result insome utilization of the optimal EPM. For example, deploying the optimalEPM may include providing the optimal EPM to a user device of a user,using the EPM in generating control signals for building equipment,saving the optimal EPM in an EPM database, analyzing the EPM to extractadditional information regarding energy prediction, generating a reportdescribing the EPM and/or results associated therewith, auditing the EPMfor accuracy, etc. As should be appreciated, deployment of the EPMperformed in step 1118 can be customizable and configurable dependent ona desired usage of the EPM. In some embodiments, step 1118 is performedby model selector 622 and/or other various components of energyprediction controller 600.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A building management system comprising: buildingequipment operable to affect a variable state or condition of abuilding; a controller comprising a processing circuit configured to:obtain an energy prediction model (EPM) for predicting energyrequirements over time; monitor one or more triggering events todetermine if the EPM should be retrained; in response to detecting thata triggering event has occurred, identify updated values of one or morehyper-parameters of the EPM; and operate the building equipment based onthe EPM.
 2. The building management system of claim 1, wherein theprocessing circuit is further configured to: calculate a performancemetric of the EPM; wherein the triggering event comprises theperformance metric exceeding a threshold value.
 3. The buildingmanagement system of claim 1, wherein the processing circuit is furtherconfigured to: compare the EPM associated with the updated values of theone or more hyper-parameters to an existing EPM to determine what EPMgenerates more accurate predictions; and deploy the EPM that generatesmore accurate predictions.
 4. The building management system of claim 1,wherein the processing circuit is further configured to pre-processoperational data of the building equipment, wherein the updated valuesof the one or more hyper-parameters are determined based on thepre-processed operational data.
 5. The building management system ofclaim 1, wherein the processing circuit is further configured to retrainthe EPM based on data describing operation of the building equipment,wherein the EPM is retrained respective to a proportion of old data andnew data.
 6. The building management system of claim 1, wherein the oneor more hyper-parameters include at least one of: a transfer learningflag; or a training window.
 7. The building management system of claim1, wherein the processing circuit is further configured to switch to anolder EPM in response to determining that the older EPM is more accuratethan the EPM.
 8. A method for operating building equipment of abuilding, the method comprising: obtaining an energy prediction model(EPM) for predicting energy requirements over time; monitoring one ormore triggering events to determine if the EPM should be retrained; inresponse to detecting that a triggering event has occurred, identifyingupdated values of one or more hyper-parameters of the EPM; and operatingthe building equipment based on the EPM to affect a variable state orcondition of the building.
 9. The method of claim 8, further comprising:calculating a performance metric of the EPM; wherein the triggeringevent comprises the performance metric exceeding a threshold value. 10.The method of claim 8, further comprising: comparing the EPM associatedwith the updated values of the one or more hyper-parameters to anexisting EPM to determine what EPM generates more accurate predictions;and deploying the EPM that generates more accurate predictions.
 11. Themethod of claim 8, further comprising pre-processing operational data ofthe building equipment, wherein the updated values of the one or morehyper-parameters are determined based on the pre-processed operationaldata.
 12. The method of claim 8, further comprising retraining the EPMbased on data describing operation of the building equipment, whereinthe EPM is retrained respective to a proportion of old data and newdata.
 13. The method of claim 8, wherein the one or morehyper-parameters include at least one of: a transfer learning flag; or atraining window.
 14. The method of claim 8, further comprising switchingto an older EPM in response to determining that the older EPM is moreaccurate than the EPM.
 15. A method for deploying an energy predictionmodel (EPM) for equipment, the method comprising: obtaining the EPM forpredicting energy requirements over time; monitoring one or moretriggering events to determine if the EPM should be retrained; inresponse to detecting that a triggering event has occurred, identifyingupdated values of one or more hyper-parameters of the EPM; and deployingthe EPM for the equipment.
 16. The method of claim 15, furthercomprising: calculating a performance metric of the EPM; wherein thetriggering event comprises the performance metric exceeding a thresholdvalue.
 17. The method of claim 15, further comprising: comparing the EPMassociated with the updated values of the one or more hyper-parametersto an existing EPM to determine what EPM generates more accuratepredictions; and deploying the EPM that generates more accuratepredictions.
 18. The method of claim 15, further comprisingpre-processing operational data of the equipment, wherein the updatedvalues of the one or more hyper-parameters are determined based on thepre-processed operational data.
 19. The method of claim 15, furthercomprising retraining the EPM based on data describing operation of theequipment, wherein the EPM is retrained respective to a proportion ofold data and new data.
 20. The method of claim 15, wherein the one ormore hyper-parameters include at least one of: a transfer learning flag;or a training window.